New fault diagnosis method of bearing based on GAF-MCNN
张超 1房颖涛 2冯建睿 3杨柯 4何世烈 5董志杰6
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作者信息
1. 西北工业大学航空学院 西安 710072;飞行器基础布局全国重点实验室 西安 710072
2. 西北工业大学航空学院 西安 710072;中国人民解放军77110部队 德阳 618000
3. 宁夏回族自治区中卫市人民政府办公室 中卫 755099
4. 中国运载火箭技术研究院 北京 100076
5. 西北工业大学航空学院 西安 710072;工业和信息化部电子第五研究所 广州 510510
6. 中国电子信息产业集团有限公司第六研究所 北京 100083
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摘要
针对轴承微小故障信号非平稳非线性且易受背景噪声干扰的特点,提出了一种基于格拉姆角场和多尺度卷积神经网络(Gramian angular field and multi-scale convolutional neural network,GAF-MCNN)的智能故障诊断方法.首先,利用分段聚合近似算法对原始振动信号进行压缩降维预处理,以减少数据存储空间和提升计算效率;然后,利用格拉姆角场算法将一维序列信号转换为二维矩阵热图,二维化后的矩阵加强了原始振动信号间的时间关系,将时间维度编码到了矩阵结构中;最后,设计了基于多尺度卷积神经网络对故障进行高效快速智能诊断.实验结果表明,GAF-MCNN诊断方法不仅克服了传统卷积神经网络诊断方法存在的计算效率较低的问题,而且诊断准确率优于单尺度卷积神经网络方法,具有较强的工程实用性.
Abstract
An intelligent fault diagnosis method based on Gramian angular field and multi-scale convolutional neural network(GAF-MCNN)was proposed to solve the non-stationary,nonlinear and easily disturbed background noise of motor bearing micro-fault signals.Firstly,the piecewise aggregation approximation algorithm is used to compress and reduce the dimension of the original vibration signals to reduce the data storage space and improve the computational efficiency.Then,the one-dimensional sequence signals are converted into two-dimensional matrix heat maps using the gramian angular field algorithm.The two-dimensional matrix strengthens the time relationship between the original vibration signals and encodes the time dimension into the matrix structure.Finally,a multi-scale convolutional neural network is designed to diagnose the fault efficiently and quickly.An example of motor bearing fault diagnosis shows that GAF-MCNN method not only overcomes the problem of low computational efficiency of traditional convolutional neural network diagnosis methods,but also has better diagnostic accuracy than single-scale convolutional neural network method,and has strong engineering practicability.
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